Yahoo Assets LLC

Oath Inc.

Verizon Media Inc.

Yan Han

Ruichen Wang

Robert Jason Harris

Timothy Michael Olson

Jian Tian

Shaima Abdul Majeed

Liang Wang

Chun Han

Xianen Qiu

One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.

Coalition network identification using iterative process

One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. First representations associated with the first entities and second representations associated with the second entities may be generated based upon the first network profile. Clusters in the first representations and/or the second representations may be identified. One or more coalition networks associated with fraudulent activity may be identified based upon the clusters.

Coalition network identification

Services often utilize scoring techniques to distinguish between user requests that comply with a usage policy of the service from those that represent a misuse of the service. Users who endeavor to misuse the service engages in probing by submitting a variety of requests to the service until one such request exhibiting a score that is within a score threshold, and then patterning further requests that misuse the service upon the successful request. Instead, when a first request from a user is identified that violates the score threshold, a score offset is selected. The scores of second and future requests by the user are adjusted by the score offset, indicating increased suspicion of the user's requests, while allowing legitimate requests that fulfill the score threshold by a larger margin. Additionally, absent further misuse, the score offset decays over time to restore trust incrementally in the legitimacy of the user's requests.

Adaptive scoring of service requests and determining whether to fulfill service requests

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Champaign, IL, United States of America

Yan Han